Importance sampling based discriminative learning for large scale offline handwritten Chinese character recognition

作者：

Highlights：

• Propose an importance sampling based discriminative learning framework for large scale classification problem.

• Introduce rejection sampling, boosting algorithm and MCE to estimate sample importance weight.

• Compare the methods on large scale character recognition problem and summarize them under the unify framework.

摘要

•Propose an importance sampling based discriminative learning framework for large scale classification problem.•Introduce rejection sampling, boosting algorithm and MCE to estimate sample importance weight.•Compare the methods on large scale character recognition problem and summarize them under the unify framework.

论文关键词：Importance sampling,Discriminative learning,Sample importance weight,Handwritten Chinese character recognition

论文评审过程：Received 13 November 2013, Revised 11 September 2014, Accepted 18 September 2014, Available online 7 October 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.09.014